doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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get_indices(i) [source]
Row and column indices of the i’th bicluster. Only works if rows_ and columns_ attributes exist. Parameters
iint
The index of the cluster. Returns
row_indndarray, dtype=np.intp
Indices of rows in the dataset that belong to the bicluster.
col_indndarray, dtype=np.intp
Indices ... | sklearn.modules.generated.sklearn.cluster.spectralcoclustering#sklearn.cluster.SpectralCoclustering.get_indices |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.cluster.spectralcoclustering#sklearn.cluster.SpectralCoclustering.get_params |
get_shape(i) [source]
Shape of the i’th bicluster. Parameters
iint
The index of the cluster. Returns
n_rowsint
Number of rows in the bicluster.
n_colsint
Number of columns in the bicluster. | sklearn.modules.generated.sklearn.cluster.spectralcoclustering#sklearn.cluster.SpectralCoclustering.get_shape |
get_submatrix(i, data) [source]
Return the submatrix corresponding to bicluster i. Parameters
iint
The index of the cluster.
dataarray-like of shape (n_samples, n_features)
The data. Returns
submatrixndarray of shape (n_rows, n_cols)
The submatrix corresponding to bicluster i. Notes Works with s... | sklearn.modules.generated.sklearn.cluster.spectralcoclustering#sklearn.cluster.SpectralCoclustering.get_submatrix |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.cluster.spectralcoclustering#sklearn.cluster.SpectralCoclustering.set_params |
sklearn.cluster.spectral_clustering(affinity, *, n_clusters=8, n_components=None, eigen_solver=None, random_state=None, n_init=10, eigen_tol=0.0, assign_labels='kmeans', verbose=False) [source]
Apply clustering to a projection of the normalized Laplacian. In practice Spectral Clustering is very useful when the struct... | sklearn.modules.generated.sklearn.cluster.spectral_clustering#sklearn.cluster.spectral_clustering |
sklearn.cluster.ward_tree(X, *, connectivity=None, n_clusters=None, return_distance=False) [source]
Ward clustering based on a Feature matrix. Recursively merges the pair of clusters that minimally increases within-cluster variance. The inertia matrix uses a Heapq-based representation. This is the structured version,... | sklearn.modules.generated.sklearn.cluster.ward_tree#sklearn.cluster.ward_tree |
class sklearn.compose.ColumnTransformer(transformers, *, remainder='drop', sparse_threshold=0.3, n_jobs=None, transformer_weights=None, verbose=False) [source]
Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed sepa... | sklearn.modules.generated.sklearn.compose.columntransformer#sklearn.compose.ColumnTransformer |
sklearn.compose.ColumnTransformer
class sklearn.compose.ColumnTransformer(transformers, *, remainder='drop', sparse_threshold=0.3, n_jobs=None, transformer_weights=None, verbose=False) [source]
Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets... | sklearn.modules.generated.sklearn.compose.columntransformer |
fit(X, y=None) [source]
Fit all transformers using X. Parameters
X{array-like, dataframe} of shape (n_samples, n_features)
Input data, of which specified subsets are used to fit the transformers.
yarray-like of shape (n_samples,…), default=None
Targets for supervised learning. Returns
selfColumnTransf... | sklearn.modules.generated.sklearn.compose.columntransformer#sklearn.compose.ColumnTransformer.fit |
fit_transform(X, y=None) [source]
Fit all transformers, transform the data and concatenate results. Parameters
X{array-like, dataframe} of shape (n_samples, n_features)
Input data, of which specified subsets are used to fit the transformers.
yarray-like of shape (n_samples,), default=None
Targets for superv... | sklearn.modules.generated.sklearn.compose.columntransformer#sklearn.compose.ColumnTransformer.fit_transform |
get_feature_names() [source]
Get feature names from all transformers. Returns
feature_nameslist of strings
Names of the features produced by transform. | sklearn.modules.generated.sklearn.compose.columntransformer#sklearn.compose.ColumnTransformer.get_feature_names |
get_params(deep=True) [source]
Get parameters for this estimator. Returns the parameters given in the constructor as well as the estimators contained within the transformers of the ColumnTransformer. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobject... | sklearn.modules.generated.sklearn.compose.columntransformer#sklearn.compose.ColumnTransformer.get_params |
property named_transformers_
Access the fitted transformer by name. Read-only attribute to access any transformer by given name. Keys are transformer names and values are the fitted transformer objects. | sklearn.modules.generated.sklearn.compose.columntransformer#sklearn.compose.ColumnTransformer.named_transformers_ |
set_params(**kwargs) [source]
Set the parameters of this estimator. Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in transformers of ColumnTransformer. Returns
self | sklearn.modules.generated.sklearn.compose.columntransformer#sklearn.compose.ColumnTransformer.set_params |
transform(X) [source]
Transform X separately by each transformer, concatenate results. Parameters
X{array-like, dataframe} of shape (n_samples, n_features)
The data to be transformed by subset. Returns
X_t{array-like, sparse matrix} of shape (n_samples, sum_n_components)
hstack of results of transformer... | sklearn.modules.generated.sklearn.compose.columntransformer#sklearn.compose.ColumnTransformer.transform |
sklearn.compose.make_column_selector(pattern=None, *, dtype_include=None, dtype_exclude=None) [source]
Create a callable to select columns to be used with ColumnTransformer. make_column_selector can select columns based on datatype or the columns name with a regex. When using multiple selection criteria, all criteria... | sklearn.modules.generated.sklearn.compose.make_column_selector#sklearn.compose.make_column_selector |
sklearn.compose.make_column_transformer(*transformers, remainder='drop', sparse_threshold=0.3, n_jobs=None, verbose=False) [source]
Construct a ColumnTransformer from the given transformers. This is a shorthand for the ColumnTransformer constructor; it does not require, and does not permit, naming the transformers. I... | sklearn.modules.generated.sklearn.compose.make_column_transformer#sklearn.compose.make_column_transformer |
class sklearn.compose.TransformedTargetRegressor(regressor=None, *, transformer=None, func=None, inverse_func=None, check_inverse=True) [source]
Meta-estimator to regress on a transformed target. Useful for applying a non-linear transformation to the target y in regression problems. This transformation can be given a... | sklearn.modules.generated.sklearn.compose.transformedtargetregressor#sklearn.compose.TransformedTargetRegressor |
sklearn.compose.TransformedTargetRegressor
class sklearn.compose.TransformedTargetRegressor(regressor=None, *, transformer=None, func=None, inverse_func=None, check_inverse=True) [source]
Meta-estimator to regress on a transformed target. Useful for applying a non-linear transformation to the target y in regression... | sklearn.modules.generated.sklearn.compose.transformedtargetregressor |
fit(X, y, **fit_params) [source]
Fit the model according to the given training data. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Target values. ... | sklearn.modules.generated.sklearn.compose.transformedtargetregressor#sklearn.compose.TransformedTargetRegressor.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.compose.transformedtargetregressor#sklearn.compose.TransformedTargetRegressor.get_params |
predict(X) [source]
Predict using the base regressor, applying inverse. The regressor is used to predict and the inverse_func or inverse_transform is applied before returning the prediction. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Samples. Returns
y_hatndarray of shape (n_... | sklearn.modules.generated.sklearn.compose.transformedtargetregressor#sklearn.compose.TransformedTargetRegressor.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.compose.transformedtargetregressor#sklearn.compose.TransformedTargetRegressor.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.compose.transformedtargetregressor#sklearn.compose.TransformedTargetRegressor.set_params |
sklearn.config_context(**new_config) [source]
Context manager for global scikit-learn configuration Parameters
assume_finitebool, default=False
If True, validation for finiteness will be skipped, saving time, but leading to potential crashes. If False, validation for finiteness will be performed, avoiding error... | sklearn.modules.generated.sklearn.config_context#sklearn.config_context |
class sklearn.covariance.EllipticEnvelope(*, store_precision=True, assume_centered=False, support_fraction=None, contamination=0.1, random_state=None) [source]
An object for detecting outliers in a Gaussian distributed dataset. Read more in the User Guide. Parameters
store_precisionbool, default=True
Specify if... | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope |
sklearn.covariance.EllipticEnvelope
class sklearn.covariance.EllipticEnvelope(*, store_precision=True, assume_centered=False, support_fraction=None, contamination=0.1, random_state=None) [source]
An object for detecting outliers in a Gaussian distributed dataset. Read more in the User Guide. Parameters
store_pr... | sklearn.modules.generated.sklearn.covariance.ellipticenvelope |
correct_covariance(data) [source]
Apply a correction to raw Minimum Covariance Determinant estimates. Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in [RVD]. Parameters
dataarray-like of shape (n_samples, n_features)
The data matrix, with p features and n samples. The ... | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.correct_covariance |
decision_function(X) [source]
Compute the decision function of the given observations. Parameters
Xarray-like of shape (n_samples, n_features)
The data matrix. Returns
decisionndarray of shape (n_samples,)
Decision function of the samples. It is equal to the shifted Mahalanobis distances. The threshold ... | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.decision_function |
error_norm(comp_cov, norm='frobenius', scaling=True, squared=True) [source]
Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm). Parameters
comp_covarray-like of shape (n_features, n_features)
The covariance to compare with.
norm{“frobenius”, “spectral”}, de... | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.error_norm |
fit(X, y=None) [source]
Fit the EllipticEnvelope model. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
yIgnored
Not used, present for API consistency by convention. | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.fit |
fit_predict(X, y=None) [source]
Perform fit on X and returns labels for X. Returns -1 for outliers and 1 for inliers. Parameters
X{array-like, sparse matrix, dataframe} of shape (n_samples, n_features)
yIgnored
Not used, present for API consistency by convention. Returns
yndarray of shape (n_samples,)
... | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.fit_predict |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.get_params |
get_precision() [source]
Getter for the precision matrix. Returns
precision_array-like of shape (n_features, n_features)
The precision matrix associated to the current covariance object. | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.get_precision |
mahalanobis(X) [source]
Computes the squared Mahalanobis distances of given observations. Parameters
Xarray-like of shape (n_samples, n_features)
The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit. Ret... | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.mahalanobis |
predict(X) [source]
Predict the labels (1 inlier, -1 outlier) of X according to the fitted model. Parameters
Xarray-like of shape (n_samples, n_features)
The data matrix. Returns
is_inlierndarray of shape (n_samples,)
Returns -1 for anomalies/outliers and +1 for inliers. | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.predict |
reweight_covariance(data) [source]
Re-weight raw Minimum Covariance Determinant estimates. Re-weight observations using Rousseeuw’s method (equivalent to deleting outlying observations from the data set before computing location and covariance estimates) described in [RVDriessen]. Parameters
dataarray-like of sha... | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.reweight_covariance |
score(X, y, sample_weight=None) [source]
Returns the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_featu... | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.score |
score_samples(X) [source]
Compute the negative Mahalanobis distances. Parameters
Xarray-like of shape (n_samples, n_features)
The data matrix. Returns
negative_mahal_distancesarray-like of shape (n_samples,)
Opposite of the Mahalanobis distances. | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.score_samples |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.covariance.ellipticenvelope#sklearn.covariance.EllipticEnvelope.set_params |
class sklearn.covariance.EmpiricalCovariance(*, store_precision=True, assume_centered=False) [source]
Maximum likelihood covariance estimator Read more in the User Guide. Parameters
store_precisionbool, default=True
Specifies if the estimated precision is stored.
assume_centeredbool, default=False
If True, ... | sklearn.modules.generated.sklearn.covariance.empiricalcovariance#sklearn.covariance.EmpiricalCovariance |
sklearn.covariance.EmpiricalCovariance
class sklearn.covariance.EmpiricalCovariance(*, store_precision=True, assume_centered=False) [source]
Maximum likelihood covariance estimator Read more in the User Guide. Parameters
store_precisionbool, default=True
Specifies if the estimated precision is stored.
assum... | sklearn.modules.generated.sklearn.covariance.empiricalcovariance |
error_norm(comp_cov, norm='frobenius', scaling=True, squared=True) [source]
Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm). Parameters
comp_covarray-like of shape (n_features, n_features)
The covariance to compare with.
norm{“frobenius”, “spectral”}, de... | sklearn.modules.generated.sklearn.covariance.empiricalcovariance#sklearn.covariance.EmpiricalCovariance.error_norm |
fit(X, y=None) [source]
Fits the Maximum Likelihood Estimator covariance model according to the given training data and parameters. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yIgnored
Not used, pre... | sklearn.modules.generated.sklearn.covariance.empiricalcovariance#sklearn.covariance.EmpiricalCovariance.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.covariance.empiricalcovariance#sklearn.covariance.EmpiricalCovariance.get_params |
get_precision() [source]
Getter for the precision matrix. Returns
precision_array-like of shape (n_features, n_features)
The precision matrix associated to the current covariance object. | sklearn.modules.generated.sklearn.covariance.empiricalcovariance#sklearn.covariance.EmpiricalCovariance.get_precision |
mahalanobis(X) [source]
Computes the squared Mahalanobis distances of given observations. Parameters
Xarray-like of shape (n_samples, n_features)
The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit. Ret... | sklearn.modules.generated.sklearn.covariance.empiricalcovariance#sklearn.covariance.EmpiricalCovariance.mahalanobis |
score(X_test, y=None) [source]
Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix. Parameters
X_testarray-like of shape (n_samples, n_features)
Test data of which we compute the likelihood, where n_samples is the number of samples and n_features is ... | sklearn.modules.generated.sklearn.covariance.empiricalcovariance#sklearn.covariance.EmpiricalCovariance.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.covariance.empiricalcovariance#sklearn.covariance.EmpiricalCovariance.set_params |
sklearn.covariance.empirical_covariance(X, *, assume_centered=False) [source]
Computes the Maximum likelihood covariance estimator Parameters
Xndarray of shape (n_samples, n_features)
Data from which to compute the covariance estimate
assume_centeredbool, default=False
If True, data will not be centered bef... | sklearn.modules.generated.sklearn.covariance.empirical_covariance#sklearn.covariance.empirical_covariance |
class sklearn.covariance.GraphicalLasso(alpha=0.01, *, mode='cd', tol=0.0001, enet_tol=0.0001, max_iter=100, verbose=False, assume_centered=False) [source]
Sparse inverse covariance estimation with an l1-penalized estimator. Read more in the User Guide. Changed in version v0.20: GraphLasso has been renamed to Graphi... | sklearn.modules.generated.sklearn.covariance.graphicallasso#sklearn.covariance.GraphicalLasso |
sklearn.covariance.GraphicalLasso
class sklearn.covariance.GraphicalLasso(alpha=0.01, *, mode='cd', tol=0.0001, enet_tol=0.0001, max_iter=100, verbose=False, assume_centered=False) [source]
Sparse inverse covariance estimation with an l1-penalized estimator. Read more in the User Guide. Changed in version v0.20: G... | sklearn.modules.generated.sklearn.covariance.graphicallasso |
error_norm(comp_cov, norm='frobenius', scaling=True, squared=True) [source]
Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm). Parameters
comp_covarray-like of shape (n_features, n_features)
The covariance to compare with.
norm{“frobenius”, “spectral”}, de... | sklearn.modules.generated.sklearn.covariance.graphicallasso#sklearn.covariance.GraphicalLasso.error_norm |
fit(X, y=None) [source]
Fits the GraphicalLasso model to X. Parameters
Xarray-like of shape (n_samples, n_features)
Data from which to compute the covariance estimate
yIgnored
Not used, present for API consistency by convention. Returns
selfobject | sklearn.modules.generated.sklearn.covariance.graphicallasso#sklearn.covariance.GraphicalLasso.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.covariance.graphicallasso#sklearn.covariance.GraphicalLasso.get_params |
get_precision() [source]
Getter for the precision matrix. Returns
precision_array-like of shape (n_features, n_features)
The precision matrix associated to the current covariance object. | sklearn.modules.generated.sklearn.covariance.graphicallasso#sklearn.covariance.GraphicalLasso.get_precision |
mahalanobis(X) [source]
Computes the squared Mahalanobis distances of given observations. Parameters
Xarray-like of shape (n_samples, n_features)
The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit. Ret... | sklearn.modules.generated.sklearn.covariance.graphicallasso#sklearn.covariance.GraphicalLasso.mahalanobis |
score(X_test, y=None) [source]
Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix. Parameters
X_testarray-like of shape (n_samples, n_features)
Test data of which we compute the likelihood, where n_samples is the number of samples and n_features is ... | sklearn.modules.generated.sklearn.covariance.graphicallasso#sklearn.covariance.GraphicalLasso.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.covariance.graphicallasso#sklearn.covariance.GraphicalLasso.set_params |
class sklearn.covariance.GraphicalLassoCV(*, alphas=4, n_refinements=4, cv=None, tol=0.0001, enet_tol=0.0001, max_iter=100, mode='cd', n_jobs=None, verbose=False, assume_centered=False) [source]
Sparse inverse covariance w/ cross-validated choice of the l1 penalty. See glossary entry for cross-validation estimator. R... | sklearn.modules.generated.sklearn.covariance.graphicallassocv#sklearn.covariance.GraphicalLassoCV |
sklearn.covariance.GraphicalLassoCV
class sklearn.covariance.GraphicalLassoCV(*, alphas=4, n_refinements=4, cv=None, tol=0.0001, enet_tol=0.0001, max_iter=100, mode='cd', n_jobs=None, verbose=False, assume_centered=False) [source]
Sparse inverse covariance w/ cross-validated choice of the l1 penalty. See glossary e... | sklearn.modules.generated.sklearn.covariance.graphicallassocv |
error_norm(comp_cov, norm='frobenius', scaling=True, squared=True) [source]
Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm). Parameters
comp_covarray-like of shape (n_features, n_features)
The covariance to compare with.
norm{“frobenius”, “spectral”}, de... | sklearn.modules.generated.sklearn.covariance.graphicallassocv#sklearn.covariance.GraphicalLassoCV.error_norm |
fit(X, y=None) [source]
Fits the GraphicalLasso covariance model to X. Parameters
Xarray-like of shape (n_samples, n_features)
Data from which to compute the covariance estimate
yIgnored
Not used, present for API consistency by convention. Returns
selfobject | sklearn.modules.generated.sklearn.covariance.graphicallassocv#sklearn.covariance.GraphicalLassoCV.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.covariance.graphicallassocv#sklearn.covariance.GraphicalLassoCV.get_params |
get_precision() [source]
Getter for the precision matrix. Returns
precision_array-like of shape (n_features, n_features)
The precision matrix associated to the current covariance object. | sklearn.modules.generated.sklearn.covariance.graphicallassocv#sklearn.covariance.GraphicalLassoCV.get_precision |
mahalanobis(X) [source]
Computes the squared Mahalanobis distances of given observations. Parameters
Xarray-like of shape (n_samples, n_features)
The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit. Ret... | sklearn.modules.generated.sklearn.covariance.graphicallassocv#sklearn.covariance.GraphicalLassoCV.mahalanobis |
score(X_test, y=None) [source]
Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix. Parameters
X_testarray-like of shape (n_samples, n_features)
Test data of which we compute the likelihood, where n_samples is the number of samples and n_features is ... | sklearn.modules.generated.sklearn.covariance.graphicallassocv#sklearn.covariance.GraphicalLassoCV.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.covariance.graphicallassocv#sklearn.covariance.GraphicalLassoCV.set_params |
sklearn.covariance.graphical_lasso(emp_cov, alpha, *, cov_init=None, mode='cd', tol=0.0001, enet_tol=0.0001, max_iter=100, verbose=False, return_costs=False, eps=2.220446049250313e-16, return_n_iter=False) [source]
l1-penalized covariance estimator Read more in the User Guide. Changed in version v0.20: graph_lasso h... | sklearn.modules.generated.sklearn.covariance.graphical_lasso#sklearn.covariance.graphical_lasso |
class sklearn.covariance.LedoitWolf(*, store_precision=True, assume_centered=False, block_size=1000) [source]
LedoitWolf Estimator Ledoit-Wolf is a particular form of shrinkage, where the shrinkage coefficient is computed using O. Ledoit and M. Wolf’s formula as described in “A Well-Conditioned Estimator for Large-Di... | sklearn.modules.generated.sklearn.covariance.ledoitwolf#sklearn.covariance.LedoitWolf |
sklearn.covariance.LedoitWolf
class sklearn.covariance.LedoitWolf(*, store_precision=True, assume_centered=False, block_size=1000) [source]
LedoitWolf Estimator Ledoit-Wolf is a particular form of shrinkage, where the shrinkage coefficient is computed using O. Ledoit and M. Wolf’s formula as described in “A Well-Co... | sklearn.modules.generated.sklearn.covariance.ledoitwolf |
error_norm(comp_cov, norm='frobenius', scaling=True, squared=True) [source]
Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm). Parameters
comp_covarray-like of shape (n_features, n_features)
The covariance to compare with.
norm{“frobenius”, “spectral”}, de... | sklearn.modules.generated.sklearn.covariance.ledoitwolf#sklearn.covariance.LedoitWolf.error_norm |
fit(X, y=None) [source]
Fit the Ledoit-Wolf shrunk covariance model according to the given training data and parameters. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yIgnored
Not used, present for AP... | sklearn.modules.generated.sklearn.covariance.ledoitwolf#sklearn.covariance.LedoitWolf.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.covariance.ledoitwolf#sklearn.covariance.LedoitWolf.get_params |
get_precision() [source]
Getter for the precision matrix. Returns
precision_array-like of shape (n_features, n_features)
The precision matrix associated to the current covariance object. | sklearn.modules.generated.sklearn.covariance.ledoitwolf#sklearn.covariance.LedoitWolf.get_precision |
mahalanobis(X) [source]
Computes the squared Mahalanobis distances of given observations. Parameters
Xarray-like of shape (n_samples, n_features)
The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit. Ret... | sklearn.modules.generated.sklearn.covariance.ledoitwolf#sklearn.covariance.LedoitWolf.mahalanobis |
score(X_test, y=None) [source]
Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix. Parameters
X_testarray-like of shape (n_samples, n_features)
Test data of which we compute the likelihood, where n_samples is the number of samples and n_features is ... | sklearn.modules.generated.sklearn.covariance.ledoitwolf#sklearn.covariance.LedoitWolf.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.covariance.ledoitwolf#sklearn.covariance.LedoitWolf.set_params |
sklearn.covariance.ledoit_wolf(X, *, assume_centered=False, block_size=1000) [source]
Estimates the shrunk Ledoit-Wolf covariance matrix. Read more in the User Guide. Parameters
Xarray-like of shape (n_samples, n_features)
Data from which to compute the covariance estimate
assume_centeredbool, default=False
... | sklearn.modules.generated.sklearn.covariance.ledoit_wolf#sklearn.covariance.ledoit_wolf |
class sklearn.covariance.MinCovDet(*, store_precision=True, assume_centered=False, support_fraction=None, random_state=None) [source]
Minimum Covariance Determinant (MCD): robust estimator of covariance. The Minimum Covariance Determinant covariance estimator is to be applied on Gaussian-distributed data, but could s... | sklearn.modules.generated.sklearn.covariance.mincovdet#sklearn.covariance.MinCovDet |
sklearn.covariance.MinCovDet
class sklearn.covariance.MinCovDet(*, store_precision=True, assume_centered=False, support_fraction=None, random_state=None) [source]
Minimum Covariance Determinant (MCD): robust estimator of covariance. The Minimum Covariance Determinant covariance estimator is to be applied on Gaussia... | sklearn.modules.generated.sklearn.covariance.mincovdet |
correct_covariance(data) [source]
Apply a correction to raw Minimum Covariance Determinant estimates. Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in [RVD]. Parameters
dataarray-like of shape (n_samples, n_features)
The data matrix, with p features and n samples. The ... | sklearn.modules.generated.sklearn.covariance.mincovdet#sklearn.covariance.MinCovDet.correct_covariance |
error_norm(comp_cov, norm='frobenius', scaling=True, squared=True) [source]
Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm). Parameters
comp_covarray-like of shape (n_features, n_features)
The covariance to compare with.
norm{“frobenius”, “spectral”}, de... | sklearn.modules.generated.sklearn.covariance.mincovdet#sklearn.covariance.MinCovDet.error_norm |
fit(X, y=None) [source]
Fits a Minimum Covariance Determinant with the FastMCD algorithm. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features. y: Ignored
Not used, present for API consistency by convention. ... | sklearn.modules.generated.sklearn.covariance.mincovdet#sklearn.covariance.MinCovDet.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.covariance.mincovdet#sklearn.covariance.MinCovDet.get_params |
get_precision() [source]
Getter for the precision matrix. Returns
precision_array-like of shape (n_features, n_features)
The precision matrix associated to the current covariance object. | sklearn.modules.generated.sklearn.covariance.mincovdet#sklearn.covariance.MinCovDet.get_precision |
mahalanobis(X) [source]
Computes the squared Mahalanobis distances of given observations. Parameters
Xarray-like of shape (n_samples, n_features)
The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit. Ret... | sklearn.modules.generated.sklearn.covariance.mincovdet#sklearn.covariance.MinCovDet.mahalanobis |
reweight_covariance(data) [source]
Re-weight raw Minimum Covariance Determinant estimates. Re-weight observations using Rousseeuw’s method (equivalent to deleting outlying observations from the data set before computing location and covariance estimates) described in [RVDriessen]. Parameters
dataarray-like of sha... | sklearn.modules.generated.sklearn.covariance.mincovdet#sklearn.covariance.MinCovDet.reweight_covariance |
score(X_test, y=None) [source]
Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix. Parameters
X_testarray-like of shape (n_samples, n_features)
Test data of which we compute the likelihood, where n_samples is the number of samples and n_features is ... | sklearn.modules.generated.sklearn.covariance.mincovdet#sklearn.covariance.MinCovDet.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.covariance.mincovdet#sklearn.covariance.MinCovDet.set_params |
class sklearn.covariance.OAS(*, store_precision=True, assume_centered=False) [source]
Oracle Approximating Shrinkage Estimator Read more in the User Guide. OAS is a particular form of shrinkage described in “Shrinkage Algorithms for MMSE Covariance Estimation” Chen et al., IEEE Trans. on Sign. Proc., Volume 58, Issue... | sklearn.modules.generated.sklearn.covariance.oas#sklearn.covariance.OAS |
sklearn.covariance.oas(X, *, assume_centered=False) [source]
Estimate covariance with the Oracle Approximating Shrinkage algorithm. Parameters
Xarray-like of shape (n_samples, n_features)
Data from which to compute the covariance estimate.
assume_centeredbool, default=False
If True, data will not be centere... | sklearn.modules.generated.oas-function#sklearn.covariance.oas |
sklearn.covariance.OAS
class sklearn.covariance.OAS(*, store_precision=True, assume_centered=False) [source]
Oracle Approximating Shrinkage Estimator Read more in the User Guide. OAS is a particular form of shrinkage described in “Shrinkage Algorithms for MMSE Covariance Estimation” Chen et al., IEEE Trans. on Sign... | sklearn.modules.generated.sklearn.covariance.oas |
error_norm(comp_cov, norm='frobenius', scaling=True, squared=True) [source]
Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm). Parameters
comp_covarray-like of shape (n_features, n_features)
The covariance to compare with.
norm{“frobenius”, “spectral”}, de... | sklearn.modules.generated.sklearn.covariance.oas#sklearn.covariance.OAS.error_norm |
fit(X, y=None) [source]
Fit the Oracle Approximating Shrinkage covariance model according to the given training data and parameters. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yIgnored
Not used, pr... | sklearn.modules.generated.sklearn.covariance.oas#sklearn.covariance.OAS.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.covariance.oas#sklearn.covariance.OAS.get_params |
get_precision() [source]
Getter for the precision matrix. Returns
precision_array-like of shape (n_features, n_features)
The precision matrix associated to the current covariance object. | sklearn.modules.generated.sklearn.covariance.oas#sklearn.covariance.OAS.get_precision |
mahalanobis(X) [source]
Computes the squared Mahalanobis distances of given observations. Parameters
Xarray-like of shape (n_samples, n_features)
The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit. Ret... | sklearn.modules.generated.sklearn.covariance.oas#sklearn.covariance.OAS.mahalanobis |
score(X_test, y=None) [source]
Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix. Parameters
X_testarray-like of shape (n_samples, n_features)
Test data of which we compute the likelihood, where n_samples is the number of samples and n_features is ... | sklearn.modules.generated.sklearn.covariance.oas#sklearn.covariance.OAS.score |
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